Comment by dadkins | Hacker News Reader

Comment by dadkins | Hacker News Reader

I think the author, like many recently exposed to the CAP theorem, is confused about the meaning of partition tolerance, leading to ridiculous conclusions.

Partition tolerance does not mean your distributed system can't be consistent and available because your network dropped one packet, or one node failed. What would be the point of such a definition? Instead, the CAP theorem implies that while the network is partitioned, consistency or availability must be sacrificed. In the case of the dropped packet, once it is retransmitted the partition is healed and progress can be made. Or in the case of the failed node, nothing says that the rest of the system can't be consistent and available, so that the system as a whole maintains that property. There is no requirement that the unavailable node be available.

Truly partition tolerant systems are those that continue to function in the face of a prolonged partition, and those are the systems that must sacrifice either consistency or availability.

What you're saying is that so long as there are no partitions, the system can be Consistent and Available, but if there's a partition, it can't.

"Consistent sometimes" is not the same thing as "Consistent" and "Available sometimes" is not the same thing as "Available" - and so "Consistent and Available sometimes" is not the same as "Consistent and Available".

I believe you might be guilty of confusing "Eventual Consistency" with "Consistency".

Funnily enough, no-one has found much use for "Eventual Availability" so far.

Assuming eventual availability can be pretty handy -- one way deal with a dependency outage is to retry with an exponential backoff. If a dependency is unavailable now, and your system keeps retrying until it is, then you are assuming your dependency will become available again eventually.

Fair point - but of course your client is not making any progress, and so the unavailability ripples up. It's unlikely that there is user facing case where this is a useful way to work, though I can see it's use in loosely coupled connections between backends.

Not quite. What I'm saying is that a dropped packet or a failed node are not partitions as far as the CAP theorem is considered.

A distributed system is considered available if "every request received by a non-failing node [results] in a response." It does not mean you cannot retransmit or retry.

Similarly, the consistency guarantee only requires that there exist a total order on operations. Failures are ok, as we're allowed to retransmit, retry, and otherwise tolerate faults. There is no inconsistency, nor is anything eventual.

My point is that a "temporary" partition is just a fault, and as long as the fault is shorter than the allowed response time of the system, it doesn't make a difference.

Yes, a system is available if one node doesn't respond and you can contact another. But that node will be unable to guarantee consistency.

If a node you can contact is required to guarantee consistency, there will be some times that it will have to refuse your request because other nodes are not contactable.

The author's point was that in any distributed system there is a non-zero probability of a network failure. While both clients and server nodes can retry connections, there is a non-zero probability that the problem will persist longer than your "availability agreement" allows. In that case, you have a choice - return potentially inconstent data or refuse the request.

What you seem to be arguing is that the probabilities of failure - in particular of repeated failure - while non-zero, are effectively zero. The author would disagree (as they point out, the probabilities combine exponentially as the number of nodes increase.) I think he's right and that you are wrong.

Paxos is the quintessential example of a highly available, consistent system. It is available as long as more than half of the nodes are up and able to communicate with each other. It remains consistent, regardless of the failure pattern. You really do only have to worry about a true network partition. This isn't a probabilistic argument in any sense.

As you have yourself pointed out, Paxos will in some cases (when less than half the nodes are up) become unavailable, but remain consistent in all cases where it is available.

So it is tolerant of partition and it sacrifices availability in favour of consistency. So it is CP, not CA.

Of course it will become either unavailable or inconsistent (or both) during a network partition. That's the essence of the CAP theorem.

But what does it mean to tolerate a partition? As if the system has a choice?

Any CA system is claiming to be consistent and available as long as the network doesn't partition. That's the strongest statement you can make under the CAP theorem, and Paxos certainly falls in that camp.

My problem with the original article was that it claimed that any individual network or node failure was a partition affecting the consistency or availability of the system. Paxos is a clear counterexample to that, as it tolerates a lot more than that without sacrificing consistency or availability.

Once the network actually partitions (or half the nodes become unreachable), then you are correct. The CAP theorem comes into play again and we must sacrifice either C or A, and Paxos chooses A.

* it never becomes inconsistent (C) * it always returns either success or failure (A) * sufficiently severe partitions kill it dead (!P)

Or does any system subject to hardware or power failure fail to count as "available"?

No, dropped packets are partitions. They really are. A partitionable network is modelled as one which may fail to deliver any subset of sent messages between nodes. The Gilbert and Lynch paper makes this explicit.

The consistency guarantee requires that RW histories are compatible with some sequentially consistent history on a non-concurrent RW register. Defining a total order on operations is sufficient, I believe, but not necessary (does it matter what order two consecutive reads happened in?).

How do you explain Paxos, then? How does a dropped packet prevent the system from responding to queries? How about if I broadcast every response 10 times to everyone I know? How many packets must be dropped for the system to be considered unavailable?

Depends on the protocol, in general.

Paxos is, fundamentally, a quorum-based system that deals with reordering of messages. It sacrifices liveness for correctness - if the proposer does not hear back from a majority of nodes (in the case of, e.g. a partition), the protocol will not complete (availability is sacrificed).

My point is not that there is a 'vital packet' in every protocol, the omission of which will cause either a lack of availability or consistency (although I can certainly design protocols that way!) - it's that for every protocol there is a network partition which causes it to be either unavailable or inconsistent. That network partition might be dropping ten messages, or just one. Retransmitting would make sense, but in real life message failures are often highly temporally correlated :(

The proof of this, by the way, is in a very famous paper by Fischer, Lynch and Patterson called "The Impossibility of Distributed Consensus With One Faulty Process". One take away is that one slow-running process can take down any protocol. It may take a few missed messages, but only a single node...

CAPL: consistency, availability, partition tolerance, latency

Paxos sacrifices latency

Incorrect, paxos sacrifices availability. Paxos is consistent but does not necessarily ever terminate.

Actually, according to your own blog post on the subject, Gilbert and Lynch define a partition tolerance as:

    “The network will be allowed to lose arbitrarily many messages sent from one node to another”

There's a huge world of difference between the network losing arbitrarily many messages, and the definition you use elsewhere in this thread: namely, any subset of packets dropped, no matter how small, counts as a network partition.

No, arbitrarily many doesn't automatically mean a huge amount :) This definition covers permanent partitions, but also encompasses temporary partitions which are effectively one dropped message or more. There exist protocols which will be broken by the loss of a single message. Paxos may not be one of them, but there is also a pattern of loss which will break that as well.

The theory behind all this really does hold this point up. I have another blog post with much more detail on the theory here: http://the-paper-trail.org/blog/?p=49, but I warn you it may be heavy going.

I'm afraid you're not quite correct. CAP says that, in the face of potentially arbitrary network partitions (which are precisely modelled by a set of dropped messages) you can be 100% consistent, or 100% available, but you can't be both.

If the network is allowed to drop packets there are times where either you must not respond to requests (as doing so would violate sequential consistency) or you must respond incorrectly, potentially with stale information.

The network partition that forces you to drop one of these guarantees might be quite dramatic - but note that, for example, a quorum system is only available on the majority side of its partition. Therefore if a single node is unable to deliver messages (due to a network partition event), it will not be able to correctly respond to requests and must either not respond to its clients or respond incorrectly.

> I'm afraid you're not quite correct.

So then what am I missing? It sounds to me like the rest of your post is reiterating what you just called incorrect.

You said that wasn't what TFA was describing, but it is.

Reply on news.ycombinator.com